Artificial intelligence drilling advisory engine in a material processing system

Abstract

Methods, systems, and computer storage media for providing drilling advisory recommendation using a drilling advisory engine in a material processing system that supports drilling operations. A drilling advisory recommendation identifies prescriptions (e.g., an arrangement of components and settings) in the material processing system to support real time optimization of drilling operations. The drilling advisory engine can be implemented as a real time prescriptive tool for operators (e.g., field personnel) to offer guidance for drilling operations. In operation, input data comprising real time sensor data of drilling operations, historical drilling information, and contextual drilling information associated with a drilling site are accessed. The input data is analyzed using two or more drilling advisory models that support generating drilling advisory recommendations that identify prescriptions for drilling operations. Based on analyzing the input data, a drilling advisory recommendation for drilling operations of the drilling site are generated. The drilling advisory recommendation is communicated.

What is claimed is: 
     
         1 . A computerized system comprising:
 one or more computer processors; and   computer memory storing computer-useable instructions that, when used by the one or more computer processors, cause the one or more computer processors to perform operations comprising:   accessing, at a drilling advisory engine, input data comprising real time sensor data of drilling operations, historical drilling information, and contextual drilling information associated with a drilling site;   analyzing the input data using two or more drilling advisory models, wherein the two or more drilling advisory models are associated with drilling operations features that support generating drilling advisory recommendations that identify prescriptions for drilling operations at drilling sites;   based on analyzing the input data using the two or more drilling advisory models, generating a drilling advisory recommendation for the drilling operations of the drilling site; and   communicating the drilling advisory recommendation.   
     
     
         2 . The system of  claim 1 , wherein the drilling advisory engine supports a plurality of drilling advisory operations comprising data-based operations, model-based operations, and analysis-based operations for drilling advisory recommendations associated with prescriptions for changing conditions and activities of drilling operations. 
     
     
         3 . The system of  claim 2 , wherein the data-based operations comprise data ingestion, data processing, and activity recognition associated with the real time sensor data of the drilling operations, the historical drilling information, and the contextual drilling information. 
     
     
         4 . The system of  claim 2 , wherein the model-based operations comprise continuously updating the two or more drilling advisory model, wherein the two or more drilling advisory models include any of the following: a machine learning model, a statistical model, or a physical model that are employed to reduce Invisible Lost Time (ILT) in drilling operations. 
     
     
         5 . The system of  claim 2 , wherein the analysis-based operations comprise contextualization and generating the drilling advisory recommendation, wherein the drilling advisory recommendations comprise dynamically targeted prescriptions for routine operations of the drilling operations. 
     
     
         6 . The system of  claim 1 , wherein the two more drilling advisory models comprise a Rate of Penetration (ROP) optimization model and a connections model, wherein the ROP optimization model support optimizing rate of penetration as a measure of drilling speed; and where connection model provides prescriptions associated with durations of routine operations of the drilling operations. 
     
     
         6 . The system of  claim 1 , wherein a material processing engine is configured to train two or more drilling advisory models that are associated with drilling operations features, wherein the two or more drilling advisory models include any of the following: a machine learning model, a statistical model, or a physical model that are employed to reduce Invisible Lost Time (ILT) in drilling operations. 
     
     
         7 . The system of  claim 1 , wherein the drilling advisory recommendation is associated with a plurality of drilling advisory recommendation interfaces, the drilling advisory recommendation interfaces comprising a drilling screen, a connection screen, a tripping screen, a casing screen, and a cleaning screen. 
     
     
         8 . The system of  claim 1 , wherein the drilling screen comprises drilling advisory interface recommendation data associated with rotations per minute, weight-on-bit, flow, and rate of penetration. 
     
     
         9 . The system of  claim 1 , wherein the connection screen comprises drilling advisory interface recommendation data associated with time left to complete connections and fastest connection. 
     
     
         10 . The system of  claim 1 , wherein the tripping screen comprises drilling advisory recommendation data associated with a minimum target speed, an actual speed, a maximum target speed, and a current trip performance. 
     
     
         11 . The system of  claim 1 , wherein the casing screen comprises drilling advisory recommendation data associated with a minimum target speed, an actual speed, a maximum target speed, and a current trip performance. 
     
     
         12 . The system of  claim 1 , wherein the cleaning screen comprises drilling advisory recommendation data associated a hookload, a free rotating TOR, a standard pipe pressure, a measured depth, and one or more alerts. 
     
     
         13 . The system of  claim 1 , wherein the two or more drilling advisory models are configured to operate in continuous competition mode, wherein a first drilling advisory model is trained with litholgic formations from previous well, a second drilling advisory model is trained with recent drilling data, and a third drilling model is trained with lithologic formations of the drilling a current well of the drilling site. 
     
     
         14 . The system of  claim 1 , the operations further comprising:
 communicating, from a material processing engine client, a request for the drilling advisory recommendation;   receive the drilling advisory recommendation for the drilling operations; and   cause generation of a drilling advisory recommendation associated with the drilling advisory recommendation.   
     
     
         15 . One or more computer-storage media having computer-executable instructions embodied thereon that, when executed by a computing system having a processor and memory, cause the processor to:
 access, at a drilling advisory engine, input data comprising real time sensor data of drilling operations, historical drilling information, and contextual drilling information associated with a drilling site;   analyze the input data using two or more drilling advisory models, wherein the two or more drilling advisory models are associated drilling operations features that support generating drilling advisory recommendations that identify prescriptions for drilling operations at drilling sites;   based on analyzing the input data using the two or more drilling advisory models, generate a drilling advisory recommendation for drilling operations of the drilling site; and   communicate the drilling advisory recommendation.   
     
     
         16 . The media of  claim 15 , wherein the processor is further caused to:
 communicate, from a material processing engine client, a request for the drilling advisory recommendation;   receive the drilling advisory recommendation for the drilling operations; and   cause generation of drilling advisory recommendation interface data associated with the drilling advisory recommendation.   
     
     
         17 . The media of  claim 16 , wherein the drilling advisory engine is configured to train two or more drilling advisory models that are associated with drilling operations features, wherein the two or more drilling advisory models any of the following: a machine learning model, a statistical model, and a physical model. 
     
     
         18 . A computer-implemented method, the method comprising:
 accessing, at a drilling advisory engine, input data comprising real time sensor data of drilling operations, historical drilling information, and contextual drilling information associated with a drilling site;   analyzing the input data using two or more drilling advisory models, wherein the two or more drilling advisory models are associated drilling operations features that support generating drilling advisory recommendations that identify prescriptions for drilling operations at drilling sites;   based on analyzing the input data using the two or more drilling advisory models, generating a drilling advisory recommendation for drilling operations of the drilling site; and   communicating the drilling advisory recommendation.   
     
     
         19 . The method of  claim 18 , wherein the drilling advisory engine supports a plurality of drilling advisory operations comprising data-based operations, model-based operations, and analysis-based operations for drilling advisory recommendations associated with prescriptions for changing conditions and activities of drilling operations. 
     
     
         20 . The method of  claim 19 , wherein the drilling advisory recommendation is associated with a plurality of drilling advisory recommendation interfaces, the drilling advisory recommendation interfaces comprising a drilling screen, a connection screen, a tripping screen, a casing screen, and a cleaning screen.